Porovnať metódy

	Multivariačný kauzálno-komparatívny výskum	Longitudinálny kauzálno-komparatívny výskum
Odbor	Dizajn výskumu	Dizajn výskumu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	Mid-20th century onward; multivariate extension systematized 1970s–1990s	1970s–1980s (as an established combined design in educational and social research)
Tvorca	Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian)	Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979)
Typ	Quantitative non-experimental comparative design	Non-experimental quantitative research design
Pôvodný zdroj	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260085594	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill. ISBN: 978-0073525532
Ďalšie názvy	multivariate causal-comparative design, MANOVA causal-comparative study, multi-outcome ex post facto research, multivariate ex post facto design	longitudinal ex post facto design, longitudinal causal-comparative design, repeated-measures causal-comparative research, prospective causal-comparative study
Príbuzné	6	4
Zhrnutie	Multivariate causal-comparative research is a quantitative, non-experimental design that investigates whether pre-existing group differences (defined by a naturally occurring categorical variable) are associated with differences across multiple outcome variables considered simultaneously. By extending the classic causal-comparative framework to several dependent variables at once, it reduces Type I error inflation and captures the correlated structure of outcomes that univariate comparisons would miss.	Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally.